Predicting geographic distribution and habitat suitability of Opuntia streptacantha in paleoclimatic, current, and future scenarios in Mexico

Abstract Mexican territory is one of the centers of origin and dispersion of the genus Opuntia, where several of its species have been an important plant resource for people in arid and semiarid zones. Opuntia streptacantha is widely distributed in Mexico; however, precise aspects of its geographic distribution and ecological status are still unknown. Here, we modeled its potential distribution under paleoclimatic, current, and future conditions through maximum entropy and predictions from 824 records and seven environmental variables. Potential distribution of O. streptacantha in the interglacial period was contracted and slightly north than current distribution, with 44,773 km2 of optimal habitat. In other past periods, the central location of potential distribution coincides with the actual current distribution, but the period of the last glacial maximum was characterized by 201 km2 of very suitable habitat, absent in interglacial, current, and future periods. The future model suggests that potential distribution will move toward the south of the Mexican territory. Synthesis and applications. The potential distribution of O. streptacantha can be applied for the conservation and management of the species, and also in the selection of areas with crassicaule scrubs for protection, conservation, and reproduction of species resistant to the hostile conditions of arid and semiarid zones of Mexican territory, where the structure and composition of the vegetation will be affected in the next 100 years.

are undoubtedly affecting the distribution of species worldwide.
However, it remains uncertain how ecosystem structure will be affected (Berg et al., 2010). To mitigate the effects of climate change on arid and semiarid ecosystems, we need to develop efficient conservation strategies by modeling the distributions of representative species in these ecosystems to identify regions where environmentally sensitive species exist or are likely to exist (Qin et al., 2017;Staudinger et al., 2012).
Geographic distribution models are relevant for ecological and biological conservation applications Phillips & Dudík, 2008) and allow to predict suitable environmental conditions for species as a function of environmental variables (Phillips et al., 2006). Ecological niche models, based on geographical records, are used to infer potential distribution pattern for a given species (Ibarra-Díaz et al., 2016;Pérez-García & Liria, 2013). This help to define the fundamental niche, which indicates the multivariate range of physiological tolerances to climatic variables, in which a given species will have positive growth rates .
Currently, the most used models for predicting species distribution are bioclimatic modeling (BIOCLIM) (Busby, 1991), domain environmental envelope (DOMAIN) (Carpenter et al., 1993), ecological niche factor analysis (ENFA) (Hirzel & Guisan, 2002), generalized additive model (GAM) (Guisan et al., 2006), genetic algorithm for rule set production (GARP) (Stockwell, 1999), and maximum entropy (MaxEnt) (Phillips et al., 2006). These models are based on the concept of ecological niche, relating biological information with environmental information, and subsequently identifying areas where there are no previous records of the species, thus obtaining the species' distribution area . Among these models, the MaxEnt model has been widely used, because it works well with incomplete data or species presence-only data (Phillips et al., 2006). MaxEnt is based on the principle of maximum entropy, a machine learning technique that uses a species' presence and background environmental data (Pearson et al., 2007), with the restriction that the expected value for each environmental variable in the distribution of a species must agree with its empirical average (Baldwin, 2009;Phillips et al., 2004Phillips et al., , 2006. The objective of this approach was to predict which areas of the region satisfy the ecological niche requirements of the species and are therefore part of its potential distribution (Anderson & Martínez-Meyer, 2013). This could be particularly relevant to design conservation strategies based on knowing the conditions where the survival of the species is suitable (Phillips et al., 2004), considering the future effects of climate change.
The family Cactaceae Juss. is the second largest neotropical family of quasi-endemic angiosperms (Hunt et al., 2006), represented by succulent plants with specialized life forms (Gibson & Nobel, 1986). They are an important floristic component of the arid and semiarid zones of America. This family includes between 1500 and 1800 species, most of them distributed in Mexico (Anderson, 2001;Majure et al., 2012). They are plants with morphophysiological adaptations that allow them to develop in arid environments (Nobel, 1996). One of the largest genera in Cactaceae is the genus Opuntia, a native lineage of Mexico, where it was originated and diversified (Barthlott & Hunt, 1993;Bravo-Hollis, 1978;García-Zambrano et al., 2009).
In this study, we modeled the potential distribution of O. streptacantha. Our goals were to identify environmental variables that are correlated with the O. streptacantha range, to predict the past, current, and future distribution, and to analyze the habitat suitability and effects of climate change on the species range. Our question was how the distribution of the species was in the past and how it could change in future scenarios. This information will impact the protection, conservation, and recovery of wild populations of this key species and their plant communities, which is also relevant for the conservation of the Chihuahuan Desert crassicaule scrubs.

| Database
Our database includes all the available geographic information from

| Environmental variables
Species distribution is an ecological process affected by temperature, precipitation, and geographic barriers such as mountain chains  (Eyring et al., 2016). The data obtained were first clipped for the surface of Mexico and then converted to ASCII file format using QGIS 3.14 (QGIS, 2020).

| Species distribution modeling
The potential distribution of O. streptacantha in different climatic scenarios was predicted using a maximum entropy algorithm in MaxEnt version 3.4.4 (Phillips et al., 2004(Phillips et al., , 2006. This program uses presence-only data to predict the distribution of a species based on the maximum entropy principle by estimating the distribution over geographical space. MaxEnt attempts to estimate a probability distribution of species presence that is as close to uniform as possible, but still subject to environmental conditions, and the resulting model is the quantification of habitat suitability for the species (Elith et al., 2011). We used "replicates" option with cross-validation to estimate model capacity, with 75% of the geographical data selected for model training and 25% for model testing (Liu et al., 2005;Morueta-Holme et al., 2010;Phillips & Dudík, 2008). The number of training repetitions was set to 10 to reduce uncertainty caused by outliers in bioclimatic variables associated with randomly selected training points. The maximum number of background points was set to 10,000 random points in Mexico. During initial model building, the percentage contribution of each bioclimatic variable was detected using the Jackknife test and variables with a low percentage contribution (<1%) (Deng et al., 2022) were eliminated. Subsequently, the MaxEnt model was refitted using six highly contributing bioclimatic variables with the data on the presence of O. streptacantha and modeling of the distribution of the species in the past, current, and future climate scenarios were performed. A threshold-independent receiver operating characteristic (ROC) analysis was carried out to evaluate the performance of MaxEnt. This curve originates the area under receiver-operating characteristic curve (AUC) statistic. The area under receiver-operating characteristic curve values range F I G U R E 1 Distribution of the 824 records of Opuntia streptacantha used in this study. from 0.5 (random) to 1.0 (perfect discrimination), where 0.5 to 0.7 model reliability is low, 0.7 to 0.9 signals a useful application of the model, and values above 0.9 are high reliability (Peterson et al., 2008;Swets, 1988). Jackknife results and response curves were used to evaluate the importance of each environmental variable for species distribution. The results of MaxEnt models were visualized in QGIS 3.14 (QGIS, 2020). Based on the classification proposed by Zhang et al. (2019), four classes of potential habitats were reclassified: no potential (<0.2), low (0.2-0.4), medium (0.4-0.6), high (0.6-0.8), and very high (0.8-1.0). In each model, the optimal distribution area was calculated and classified as high or very high suitability habitat (0.6-1.0).   (Table 1).   (Table 1).

| Predicted future potential distribution
The future predictions for the years 2050 (Figure 6b habitat than the current climate and paleoclimatic scenarios, with 54,019 km 2 (12.8% higher than current), 50,712 km 2 (7.1% higher than current), and 52,138 km 2 (10.2% higher than current), respec-

| DISCUSS ION
The global increasing average temperature threats the growth and survival of many wild species and their habitat (Brummitt & Bachman, 2010), as is the case of the O. streptacantha. Although tolerance to extreme temperatures has been reported for this genus (Nobel & De La Barrera, 2003),  Opuntia, which grows well at 27-30°C and whose roots and stems are sensitive to temperature changes (Drennan & Nobel, 1998;Nobel & Bobich, 2002). These variables are relevant for photosynthesis and water uptake (Austin, 2002;Low et al., 2021

| Climate scenarios
The potential distributions estimated for O. streptacantha in the past, current, and future scenarios, predicted by our models, are consistent with the expected effect of climate change on the species over time. Biodiversity around the world is vulnerable to the threats of climate change, which can compromise its distributional ranges (Bellard et al., 2012). The last 2.5 million years were times of intense environmental climatic changes, climatic cycles of growth, and the decline of the polar ice cap that occurred in North America, affecting global climatic conditions. Populations decreased in size, shrinking F I G U R E 4 Response curves of the six most important environmental factors in modeling habitat distribution for Opuntia streptacantha.
to so-called Pleistocene refugia where they survived these difficult periods of climate change . It has been argued that the climate of the LIG was warmest and wettest than the current, being the longest warm period (Wu et al., 2006(Wu et al., , 2007; central Mexico was warmer and more humid in this period than today, with more precipitation in its coldest period (Metcalfe, 2006). The C 3 plants dominated the past and only a small group of CAM plants existed (Chen et al., 2011). According to our results, O. streptacantha populations that survived the LIG could occupy areas where they found refuge from the warm and humid conditions. However, the climatic change during the LGM to coldest and driest conditions could produce a sudden demographic expansion in some species and the glacial descent of the mountains allowed seed-mediated gene flow (Ornelas & Rodríguez-Gómez, 2015). This could trigger the diver-  the LIG period and expansion in the LGM period has been reported for other taxa adapted to arid ecosystems such as Melampodium leucanthum (Rebernig et al., 2010), Astrophytum (Vázquez-Lobo et al., 2015), Agave lechuguilla (Scheinvar et al., 2017), and Agave kerchovei , and agree with the idea of modernization of North American deserts during the dry intervals of the Quaternary period (De-Nova et al., 2020;Graham, 2010).
The CAM plants are efficient in use of water and in maintaining carbon gain in view of low extreme water stress Hernández-Hernández et al., 2014;Reyes-Agüero et al., 2006;Scheinvar et al., 2017).
Our results revealed a larger current potential area than the real distribution for O. streptacantha through northern and southern Mexico. The potential distribution of its optimal habitat is consistent with the distribution mentioned by Bravo-Hollis (1978) andScheinvar et al. (2008). This is related to the geological history of the territory, soils, and elevation, which reflect the diverse landscape with rich plant associations where the species occurs (Dávila et al., 2002;Morrone, 2019;Valiente-Banuet et al., 2009). Individuals of O. streptacantha mainly grow on hillsides and slopes, where temperature, humidity, and fluctuate more than in lowlands. Climatic fluctuations have long been suggested as a key factor in cactus family diversification and distribution (Homer et al., 2020;Nyman et al., 2012), with temperature being one of the main determinants for distribution (Fitter & Hay, 2002).
The long dispersal distances of O. streptacantha in central Mexico has been argued to have benefited from migratory Pleistocene megafauna such as the woolly mammoth (Majure et al., 2012), as well as countless herbivores that make up the local fauna such as turtles, birds, rabbits, bats, and even coyotes (Mellink & Riojas-López, 2002), promoting a more southern distribution in the present. According to our predicted future scenarios,

ACK N OWLED G M ENTS
The first author received a grant from CONACYT (Graduate Studies Scholarship 747245). This research was funded by the international cooperative research of Rural Development Administration (RDA) from the Republic of Korea (grant PJ012429012016 to PD-S). We appreciate the support of the Desert Botanical Garden.

CO N FLI C T O F I NTER E S T S TATEM ENT
We declare no conflict of interest.

DATA AVA I L A B I L I T Y S TAT E M E N T
Geographical data and maps are available via ZENODO https://doi. org/10.5281/zenodo.7796359.